BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Computational Optimisation Group - ECPv5.16.1.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Computational Optimisation Group X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk X-WR-CALDESC:Events for Computational Optimisation Group BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20180325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20181028T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20180425T140000 DTEND;TZID=Europe/Paris:20180425T150000 DTSTAMP:20240329T140041 CREATED:20180319T142205Z LAST-MODIFIED:20180319T142644Z UID:1056-1524664800-1524668400@optimisation.doc.ic.ac.uk SUMMARY:Seminar: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers DESCRIPTION:Title: Computing Pessimistic Leader-Follower Equilibria with Multiple Followers\nSpeaker: Dr Stefano Coniglio\nAffiliation: Dept. of Mathematical Sciences\, Southampton University\nLocation: Room 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. We investigate the problem of computing a Leader-Follower equilibrium in Stackelberg games where two or more followers react to the strategy chosen by the (single) leader by playing a Nash Equilibrium. We consider two natural cases\, the optimistic one where the followers select a Nash Equilibrium maximizing the leader’s utility and the pessimistic one where they select an equilibrium minimizing the leader’s utility. We first illustrate that\, in both cases\, computing a Leader-Follower Nash equilibrium is NP-hard and not in Poly-APX unless P=NP\, and that even deciding whether one of the leader’s actions would be played with strictly positive probability is NP-hard. We then focus on the pessimistic case with followers restricted to pure strategies\, showing that this problem too is NP-hard and not in Poly-APX unless P=NP. After casting it as a pessimistic bilevel programming problem\, we propose an exact implicit enumeration algorithm for its solution. In particular\, our algorithm is capable of computing the maximum of the problem and\, for the general case where the former only admits a supremum\, an alpha-approximate strategy for any nonnegative alpha. Experimental results are presented and illustrated\, showing the viability of the proposed approach. \nThis is joint work with Nicola Gatti and Alberto Marchesi. \nAbout the speaker. Dr Stefano Coniglio is a Lecturer in Operational Research within Mathematical Sciences at the University of Southampton. Dr Stefano Coniglio joined the University of Southampton as Lecturer in Operational Research within Mathematical Sciences in February 2016. Prior to that\, he served as Research Associate at the RWTH Aachen University\, Germany\, and as Postdoctoral Researcher at Politecnico di Milano\, Italy\, where he received his Ph.D. in Information Technology in 2011. His research focuses on exact methods for the solution of mathematical programming and combinatorial optimization problems. Recently\, he has been most active in bilevel programming\, robust optimization\, and methodological aspects of cutting plane generation\, with applications to algorithmic game theory\, optimization in telecommunication networks\, energy production planning\, and data mining. URL:https://optimisation.doc.ic.ac.uk/event/seminar-computing-pessimistic-leader-follower-equilibria-with-multiple-followers/ END:VEVENT END:VCALENDAR